XGboost and random forest algorithm for supply fraud forecasting

Abstract

With the comprehensive development and application of machine learning, forecasting supply by machine learning has achieved good results. However, as supply chain fraud is more complicated, using a simple machine learning algorithm can no longer achieve better performance for supply fraud forecasting. In this paper, we propose an XGboost and random forest algorithm to predict supply fraud. This algorithm first uses the random forest to filter out the unimportant variables, get essential variables, and then build an XGBoost model to predict supply chain fraud. Experimental results demonstrate that our proposed XGboost and random forest algorithm achieves great efficiency for supply fraud prediction than logistic, random forest, and XGBoost algorithms.